# Difficulty Modelling in Mobile Puzzle Games: An Empirical Study on Different Methods to Combine Player Analytics and Simulated Data

arXiv: https://arxiv.org/abs/2401.17436 · HTML: https://mlsanity.com/paper/2401.17436/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 30 Jan 2024.

## Builds on (most cited first, 12 of 12)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Augmenting Automated Game Testing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.15819/) (2021)
- [Automated Video Game Testing Using Synthetic and Human-Like Agents](https://mlsanity.com/paper/1906.00317/) (2019)
- [Winning Isn't Everything: Enhancing Game Development with Intelligent Agents](https://mlsanity.com/paper/1903.10545/) (2019)
- [Automated Playtesting of Matching Tile Games](https://mlsanity.com/paper/1907.06570/) (2019)
- [Strategies for Using Proximal Policy Optimization in Mobile Puzzle Games](https://mlsanity.com/paper/2007.01542/) (2020)
- [Predicting Game Engagement and Difficulty Using AI Players](https://mlsanity.com/paper/2107.12061/) (2021)
- [DL-DDA -- Deep Learning based Dynamic Difficulty Adjustment with UX and Gameplay constraints](https://mlsanity.com/paper/2106.03075/) (2021)
- [Fast Game Content Adaptation Through Bayesian-based Player Modelling](https://mlsanity.com/paper/2105.08484/) (2021)
- [Statistical Modelling of Level Difficulty in Puzzle Games](https://mlsanity.com/paper/2107.03305/) (2021)
- [Estimating player completion rate in mobile puzzle games using reinforcement learning](https://mlsanity.com/paper/2306.14626/) (2023)
- [Personalized Game Difficulty Prediction Using Factorization Machines](https://mlsanity.com/paper/2209.13495/) (2022)

## Built on by (most cited first, 1 of 1)

- [BiRating -- Iterative averaging on a bipartite graph of Beat Saber scores, player skills, and map difficulties](https://mlsanity.com/paper/2502.19742/) (2025)

## Abstract (arXiv)

Difficulty is one of the key drivers of player engagement and it is often one of the aspects that designers tweak most to optimise the player experience; operationalising it is, therefore, a crucial task for game development studios. A common practice consists of creating metrics out of data collected by player interactions with the content; however, this allows for estimation only after the content is released and does not consider the characteristics of potential future players. In this article, we present a number of potential solutions for the estimation of difficulty under such conditions, and we showcase the results of a comparative study intended to understand which method and which types of data perform better in different scenarios. The results reveal that models trained on a combination of cohort statistics and simulated data produce the most accurate estimations of difficulty in all scenarios. Furthermore, among these models, artificial neural networks show the most consistent results.